Task (computing)

About: Task (computing) is a research topic. Over the lifetime, 9718 publications have been published within this topic receiving 129364 citations.

Determinating Timing Channels in Compute Clouds

Abstract: Timing side-channels represent an insidious security challenge for cloud computing, because: (a) massive parallelism in the cloud makes timing channels pervasive and hard to control; (b) timing channels enable one customer to steal information from another without leaving a trail or raising alarms; (c) only the cloud provider can feasibly detect and report such attacks, but the provider's incentives are not to; and (d) resource partitioning schemes for timing channel control undermine statistical sharing efficiency, and, with it, the cloud computing business model. We propose a new approach to timing channel control, using provider-enforced deterministic execution instead of resource partitioning to eliminate timing channels within a shared cloud domain. Provider-enforced determinism prevents execution timing from affecting the results of a compute task, however large or parallel, ensuring that a task's outputs leak no timing information apart from explicit timing inputs and total compute duration. Experiments with a prototype OS for deterministic cloud computing suggest that such an approach may be practical and efficient. The OS supports deterministic versions of familiar APIs such as processes, threads, shared memory, and file systems, and runs coarse-grained parallel tasks as efficiently and scalably as current timing channel-ridden systems.

PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing.

Abstract: Depth estimation and scene parsing are two particularly important tasks in visual scene understanding. In this paper we tackle the problem of simultaneous depth estimation and scene parsing in a joint CNN. The task can be typically treated as a deep multi-task learning problem [42]. Different from previous methods directly optimizing multiple tasks given the input training data, this paper proposes a novel multi-task guided prediction-and-distillation network (PAD-Net), which first predicts a set of intermediate auxiliary tasks ranging from low level to high level, and then the predictions from these intermediate auxiliary tasks are utilized as multi-modal input via our proposed multi-modal distillation modules for the final tasks. During the joint learning, the intermediate tasks not only act as supervision for learning more robust deep representations but also provide rich multi-modal information for improving the final tasks. Extensive experiments are conducted on two challenging datasets (i.e. NYUD-v2 and Cityscapes) for both the depth estimation and scene parsing tasks, demonstrating the effectiveness of the proposed approach.

SYNWORK1: A PC-based tool for assessment of performance in a simulated work environment

Abstract: SYNWORK1 is a computer-based performance task that requires subjects to work simultaneously on four distinct subtasks involving memory, arithmetic processing, and visual and auditory monitoring. Difficulty levels, the payoff matrix, feedback levels, and component subtask mix are user selectable. Detailed data are automatically collected, and a suite of data analysis programs is available. SYNWORK1 is being used in sleep-deprivation and circadian desynchronization experiments and in a variety of clinical research applications. Representative data from a sleep-deprivation experiment are presented to demonstrate the sensitivity of the technique. The strategy used for programming concurrent tasks on a PC is described.

Universal worklist service and user interface

Abstract: Work items are collected from one or more work item providers for presentation to a workflow participant. A work item provider is typically a workflow management system ('WFMS'), but other providers of work items might exist that generate work items and may or may not have their own local worklist. Multiple independent WFMS's of multiple types might provide work items to a universal worklist ('UWL') service that integrates the work items from all of the providers. Additional integration might be provided between the UWL and work item providers, such as for controlling work item execution with a sub-workflow through an ad-hoc workflow engine. Additional integration provides for custom attributes. Each work item has a type that is used to determine handling for that work item. A universal work list service includes storage for work items, wherein a work item represents a task generated as part of a process flow. An engine supporting the UWL service might track item types for work items and might obtain attribute values for attributes of the work item according to the work item's type.

Development and Evaluation of Search Tasks for IIR Experiments using a Cognitive Complexity Framework

Abstract: One of the most challenging aspects of designing interactive information retrieval (IIR) experiments with users is the development of search tasks. We describe an evaluation of 20 search tasks that were designed for use in IIR experiments and developed using a cognitive complexity framework from educational theory. The search tasks represent five levels of cognitive complexity and four topical domains. The tasks were evaluated in the context of a laboratory IIR experiment with 48 participants. Behavioral and self-report data were used to characterize and understand differences among tasks. Results showed more cognitively complex tasks required significantly more search activity from participants (e.g., more queries, clicks, and time to complete). However, participants did not evaluate more cognitively complex tasks as more difficult and were equally satisfied with their performances across tasks. Our work makes four contributions: (1) it adds to what is known about the relationship among task, search behaviors and user experience; (2) it presents a framework for task creation and evaluation; (3) it provides tasks and questionnaires that can be reused by others and (4) it raises questions about findings and assumptions of many recent studies that only use behavioral signals from search logs as evidence for task difficulty and searcher satisfaction, as many of our results directly contradict these findings.